## [1] "2024-04-15 11:37:58 CEST"
## [1] "explicated variable of regression : canopy_cover_rain_only_epsilon"
## [1] "for all_Africa_regression_canopy_cover.RDS"
## [2] "for Guinean_forest-savanna_regression_canopy_cover.RDS"
## [3] "for Northern_Congolian_Forest-Savanna_regression_canopy_cover.RDS"
## [4] "for Sahelian_Acacia_savanna_regression_canopy_cover.RDS"
## [5] "for Southern_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [6] "for West_Sudanian_savanna_regression_canopy_cover.RDS"
## [7] "for Western_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "########################################"
## [1] "########################################"
## [1] "below, stancode for all_Africa_regression_canopy_cover.RDS"
## // generated with brms 2.20.4
## functions {
## }
## data {
## int<lower=1> N; // total number of observations
## vector[N] Y; // response variable
## int<lower=1> K; // number of population-level effects
## matrix[N, K] X; // population-level design matrix
## int<lower=1> Kc; // number of population-level effects after centering
## int prior_only; // should the likelihood be ignored?
## }
## transformed data {
## matrix[N, Kc] Xc; // centered version of X without an intercept
## vector[Kc] means_X; // column means of X before centering
## for (i in 2:K) {
## means_X[i - 1] = mean(X[, i]);
## Xc[, i - 1] = X[, i] - means_X[i - 1];
## }
## }
## parameters {
## vector[Kc] b; // regression coefficients
## real Intercept; // temporary intercept for centered predictors
## real<lower=0> phi; // precision parameter
## }
## transformed parameters {
## real lprior = 0; // prior contributions to the log posterior
## lprior += student_t_lpdf(Intercept | 3, 0, 2.5);
## lprior += gamma_lpdf(phi | 0.01, 0.01);
## }
## model {
## // likelihood including constants
## if (!prior_only) {
## // initialize linear predictor term
## vector[N] mu = rep_vector(0.0, N);
## mu += Intercept + Xc * b;
## mu = inv_logit(mu);
## target += beta_lpdf(Y | mu * phi, (1 - mu) * phi);
## }
## // priors including constants
## target += lprior;
## }
## generated quantities {
## // actual population-level intercept
## real b_Intercept = Intercept - dot_product(means_X, b);
## }
## [1] "########################################"
## [1] "########################################"
## [1] "########################################"
## [1] " "
## [1] " "
## [1] "all_Africa_regression_canopy_cover.RDS"
## [1] "########################################"
## Family: beta
## Links: mu = logit; phi = identity
## Formula: canopy_cover ~ mean_precip_std
## Data: table_region (Number of observations: 13362)
## Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
## total post-warmup draws = 2400
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -3.03 0.01 -3.06 -3.01 1.00 2413 2409
## mean_precip_std 0.41 0.01 0.40 0.43 1.00 2426 2290
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi 7.92 0.12 7.68 8.16 1.00 2548 2167
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## [1] "########################################"
## [1] "Guinean_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## Family: beta
## Links: mu = logit; phi = identity
## Formula: canopy_cover ~ mean_precip_std
## Data: table_region (Number of observations: 1725)
## Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
## total post-warmup draws = 2400
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -2.12 0.06 -2.24 -2.01 1.00 2515 2500
## mean_precip_std 0.13 0.03 0.08 0.18 1.00 2546 2330
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi 4.34 0.16 4.04 4.65 1.00 2414 2410
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## [1] "########################################"
## [1] "Northern_Congolian_Forest-Savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## Family: beta
## Links: mu = logit; phi = identity
## Formula: canopy_cover ~ mean_precip_std
## Data: table_region (Number of observations: 243)
## Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
## total post-warmup draws = 2400
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -1.13 0.36 -1.82 -0.41 1.00 2475 2351
## mean_precip_std -0.23 0.17 -0.56 0.08 1.00 2426 2371
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi 5.46 0.50 4.52 6.49 1.00 2032 2455
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## [1] "########################################"
## [1] "Sahelian_Acacia_savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## Family: beta
## Links: mu = logit; phi = identity
## Formula: canopy_cover ~ mean_precip_std
## Data: table_region (Number of observations: 5563)
## Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
## total post-warmup draws = 2400
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -4.62 0.02 -4.67 -4.58 1.00 2502 2204
## mean_precip_std 0.84 0.03 0.78 0.90 1.00 2303 2219
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi 85.14 2.04 81.07 89.10 1.00 2267 2239
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## [1] "########################################"
## [1] "Southern_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## Family: beta
## Links: mu = logit; phi = identity
## Formula: canopy_cover ~ mean_precip_std
## Data: table_region (Number of observations: 47)
## Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
## total post-warmup draws = 2400
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -1.42 1.31 -4.00 1.04 1.00 2445 2233
## mean_precip_std -0.71 0.65 -1.94 0.58 1.00 2404 2397
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi 5.61 1.59 3.03 9.24 1.00 2248 2151
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## [1] "########################################"
## [1] "West_Sudanian_savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## Family: beta
## Links: mu = logit; phi = identity
## Formula: canopy_cover ~ mean_precip_std
## Data: table_region (Number of observations: 3277)
## Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
## total post-warmup draws = 2400
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -3.60 0.04 -3.68 -3.52 1.00 2390 2212
## mean_precip_std 0.86 0.03 0.79 0.92 1.00 2264 2165
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi 9.43 0.28 8.91 9.99 1.00 2621 2370
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## [1] "########################################"
## [1] "Western_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## Family: beta
## Links: mu = logit; phi = identity
## Formula: canopy_cover ~ mean_precip_std
## Data: table_region (Number of observations: 259)
## Draws: 3 chains, each with iter = 10000; warmup = 2000; thin = 10;
## total post-warmup draws = 2400
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -2.25 0.23 -2.70 -1.78 1.00 2441 2332
## mean_precip_std 0.13 0.15 -0.17 0.42 1.00 2502 2342
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi 3.23 0.33 2.63 3.91 1.00 2626 2427
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
## [1] "########################################"
## [1] "Beta regressions for all_Africa_regression_canopy_cover.RDS"
## [2] "Beta regressions for Guinean_forest-savanna_regression_canopy_cover.RDS"
## [3] "Beta regressions for Northern_Congolian_Forest-Savanna_regression_canopy_cover.RDS"
## [4] "Beta regressions for Sahelian_Acacia_savanna_regression_canopy_cover.RDS"
## [5] "Beta regressions for Southern_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [6] "Beta regressions for West_Sudanian_savanna_regression_canopy_cover.RDS"
## [7] "Beta regressions for Western_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "########################################"
## [1] "########################################"
## [1] "all_Africa_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 13362 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"
## Le chargement a nécessité le package : gtools
##
## Attachement du package : 'gtools'
## Les objets suivants sont masqués depuis 'package:brms':
##
## ddirichlet, rdirichlet
## [1] "mean(table_region$canopy_cover)"
## [1] 0.05362057
## [1] "sd(table_region$canopy_cover)"
## [1] 0.09756511
## [1] "Guinean_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 1725 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"
## [1] "mean(table_region$canopy_cover)"
## [1] 0.1324814
## [1] "sd(table_region$canopy_cover)"
## [1] 0.14466
## [1] "Northern_Congolian_Forest-Savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 243 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"
## [1] "mean(table_region$canopy_cover)"
## [1] 0.1659144
## [1] "sd(table_region$canopy_cover)"
## [1] 0.1334132
## [1] "Sahelian_Acacia_savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 5563 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"
## [1] "mean(table_region$canopy_cover)"
## [1] 0.006161202
## [1] "sd(table_region$canopy_cover)"
## [1] 0.02202149
## [1] "Southern_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 47 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"
## [1] "mean(table_region$canopy_cover)"
## [1] 0.05084602
## [1] "sd(table_region$canopy_cover)"
## [1] 0.1063901
## [1] "West_Sudanian_savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 3277 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"
## [1] "mean(table_region$canopy_cover)"
## [1] 0.05400694
## [1] "sd(table_region$canopy_cover)"
## [1] 0.08641946
## [1] "Western_Congolian_forest-savanna_regression_canopy_cover.RDS"
## [1] "########################################"
## [1] "dim(linear_predictors_for_one_beta_draw_per_column)"
## [1] 259 2400
## [1] "(nb_donnes I * nb_iter_mcmc J)"
## [1] "mean(table_region$canopy_cover)"
## [1] 0.1089882
## [1] "sd(table_region$canopy_cover)"
## [1] 0.1649516
